Low-carbon park comprehensive energy data grabbing method and system

Abstract

The application discloses a kind of low-carbon park comprehensive energy data acquisition method and system, comprising: collecting low-carbon park data, pre-processing is carried out.The correlation and dependency of the data after pre-processing and overall energy consumption are comprehensively evaluated.Based on the correlation and dependency of the data and overall energy consumption, an energy data model is established to analyze the dynamic change relationship between different energy types and overall energy consumption in the park.According to the dynamic change relationship, energy use optimization suggestions are generated.The application effectively captures the timing changes and nonlinear dependencies of different energy systems and overall energy consumption, enabling more accurate inter-system interaction assessment.The correlation and dependency are introduced into energy consumption prediction and dispatch optimization, and the genetic algorithm is used to dynamically adjust the system weight, providing an intelligent energy dispatching scheme that makes the load response of the energy system more flexible and accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-carbon park energy management, in particular to a low-carbon park comprehensive energy data acquisition method and system. BACKGROUND

[0002] With the global emphasis on carbon emission control, low-carbon parks have become one of the important ways to achieve sustainable development. In low-carbon parks, various energy systems (such as photovoltaic power generation, battery energy storage, heat pump systems, etc.) are widely used to provide electricity, refrigeration and heat. However, with the increase of equipment and energy types in the park, how to efficiently collect, integrate and analyze these energy data has become a technical challenge. At present, the energy management system in the park mostly relies on single or scattered energy data monitoring scheme, which is difficult to comprehensively evaluate the synergistic effect and dynamic change of each energy system. The existing energy management method mostly uses static data model, which can only statistically analyze the historical energy consumption, lacks real-time data acquisition and dynamic adjustment mechanism, and is difficult to realize efficient and accurate energy scheduling. In addition, the existing technology often cannot effectively analyze the interdependence between different energy systems, and fails to fully utilize complex calculation models such as information entropy to analyze the system dependence, resulting in the inability to dynamically adapt to the real-time demand of the park in energy optimization.

[0003] The existing technology has deficiencies in many aspects. First, the collection and preprocessing process of energy data lacks flexibility, which cannot meet the diversified needs of various energy systems in the park, such as real-time collection and cleaning of multi-dimensional data such as temperature, voltage, wind volume, etc. Second, in terms of correlation and dependence analysis, the existing technology usually uses linear model, ignoring the nonlinear dynamic interaction between different systems. In addition, the optimization algorithm in the existing technology mostly adopts traditional weighted model, without introducing intelligent heuristic algorithm, which cannot provide dynamic optimization suggestions for complex multi-system energy scheduling. These limitations result in low energy management efficiency, and the system is difficult to respond to the changes of energy consumption demand in the park in real time. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the present application solves the problem of difficult real-time dynamic data analysis and energy scheduling optimization in the prior art. The existing technology mostly relies on static historical data, which cannot timely respond to the dynamic changes of various energy systems in the park. In addition, the linear optimization algorithm in the traditional technology is difficult to cope with complex multi-system interaction and nonlinear dependence, and the present application solves the problems of inaccurate system energy consumption prediction and rigid energy scheduling through heuristic optimization by genetic algorithm.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a low-carbon park comprehensive energy data acquisition method, comprising: collecting low-carbon park data, and preprocessing.

[0007] Comprehensively evaluating the correlation and dependency of the preprocessed data and overall energy consumption.

[0008] Based on the correlation and dependency of the data and overall energy consumption, an energy data model is established to analyze the dynamic change relationship between different energy types and overall energy consumption of the park.

[0009] Generating energy use optimization suggestions according to the dynamic change relationship.

[0010] As a preferred scheme of the low-carbon park comprehensive energy data acquisition method, the collection of low-carbon park data and preprocessing includes collecting basic data type system, photovoltaic system, battery system, heat pump system, water storage system, wind disc and lighting data in the park.

[0011] The basic data type system includes temperature.

[0012] The photovoltaic system includes current and temperature.

[0013] The battery system includes current, voltage, temperature and remaining power.

[0014] The heat pump system includes water temperature and flow rate of the primary side and the secondary side.

[0015] The water storage system includes inlet and outlet water temperature and stratified water temperature.

[0016] The wind disc includes temperature and wind volume.

[0017] The lighting data includes illumination, brightness and power.

[0018] The preprocessing includes data cleaning and data normalization.

[0019] The preprocessing includes filling missing values and data standardization.

[0020] The missing value filling method is mean filling, and the data standardization is represented as:

[0021]

[0022] Wherein, x i ′ represents the standardized data, x i represents the original data, x min represents the minimum value in the data, and x max represents the maximum value in the data.

[0023] As a preferred scheme of the low-carbon park comprehensive energy data grabbing method, the correlation and dependency of the preprocessed data and the overall energy consumption in the comprehensive evaluation includes that the correlation is used to measure the linear relationship between each energy system and the overall energy consumption, and is calculated by using a Pearson correlation coefficient and is expressed as:

[0024]

[0025] wherein x i represents the energy data of the i-th system, y i represents the overall energy consumption data at the i-th moment, represents the mean value of the system energy data, represents the mean value of the overall energy consumption data.

[0026] A time-weighted dynamic correlation coefficient is introduced, a decreasing exponential decay function is applied in the time dimension, and the weighting term is expressed as:

[0027]

[0028] wherein a represents a time decay coefficient, t n represents the current moment, t i represents the i-th moment.

[0029] The Pearson correlation coefficient after introducing the time decay is expressed as:

[0030]

[0031] The Pearson correlation coefficient after introducing the time decay has a value range of -1-1, close to 1 indicates strong positive correlation between the energy system and the overall energy consumption, close to -1 indicates strong negative correlation, and close to 0 indicates no correlation.

[0032] As a preferred scheme of the low-carbon park comprehensive energy data grabbing method, the correlation and dependency of the preprocessed data and the overall energy consumption in the comprehensive evaluation further includes that the dependency reflects the influence degree of a certain system on the overall energy consumption, and whether the system can determine the change of the overall energy consumption. The conditional entropy and mutual information based on information theory are used for calculation. The original conditional entropy formula is the uncertainty of the system X under the premise that the system Y is known, and is expressed as:

[0033] H(X|Y(=H(X|Y)-H(Y)

[0034] wherein H(X|Y) represents the conditional entropy, represents the uncertainty of X under the condition that Y is known, H(X,Y) represents the joint entropy, represents the uncertainty of X and Y together, and H(Y) represents the entropy of Y, represents the uncertainty of Y.

[0035] A nonlinear dependence index is introduced to strengthen the capture of nonlinear dependence relationship by adding a mutual information filter function, which is expressed as:

[0036]

[0037] Where D(X, Y) represents the dependence of system X on the overall energy consumption Y, I(X; Y) represents the mutual information between X and Y, and captures the information dependence relationship between them. H(X|Y i-1 ) represents the conditional entropy at the previous moment, which is used to construct the time series dependence relationship of the system. β represents the contribution degree of control mutual information to the dependence, which ensures that the dependence is higher when the mutual information is larger. N represents the total number of sampling points.

[0038] The optimized dependence value range is 0-1, wherein 0 represents no dependence relationship, and 1 represents complete dependence relationship. The closer the value is to 1, the greater the influence of the system on the overall energy consumption.

[0039] As a preferred scheme of the low-carbon park comprehensive energy data grabbing method, the energy data model is established, including constructing a comprehensive energy consumption model based on correlation and dependence, and analyzing the dynamic change relationship between different energy types and the overall energy consumption of the park.

[0040] Based on the correlation coefficient and the data of each energy system, the overall energy consumption is calculated, which is expressed as:

[0041]

[0042] Wherein, represents the predicted overall energy consumption at the t time, m represents the number of energy systems, w i represents the weight of the i th system, reflecting its influence on the overall energy consumption. r dynamic,i (t) represents the dynamic correlation coefficient of the i th system at the t time, E i (t) represents the energy consumption data of the i th system at the t time.

[0043] In the dependence analysis, the energy consumption contribution in the model is adjusted according to the dependence of each system on the overall energy consumption. The dependence is used to adjust the influence degree of each system, which finally affects its weight. We nonlinearly adjust the weight in energy consumption prediction, which is expressed as:

[0044]

[0045] Wherein, w i ' represents the weight of the i th system after the dependence adjustment, D(X i , Y) represents the dependence of the i th system on the overall energy consumption, w irepresents the initial weight, the proportion of the influence of each system on the overall energy consumption without adjustment.

[0046] As a preferred scheme of the low-carbon park comprehensive energy data grabbing method, the method further comprises: introducing a genetic algorithm to optimize the weight w and the dependence degree parameter in the model, so that the overall energy consumption prediction is more accurate, and an optimal energy scheduling scheme is provided.

[0047] Randomly generate a set of candidate solutions of weights {w i} and dependence degrees {D(X i ,Y)}, each solution representing a different energy scheduling scheme.

[0048] Define a fitness function f(w1,w2,…,w m ,D1,D2,…,D m ) as the reciprocal of the energy consumption prediction error, the goal being to minimize the prediction error, which is represented as:

[0049]

[0050] wherein E true (t) represents the actual energy consumption value, and E represents the predicted energy consumption value.

[0051] Select individuals with high fitness as parents to enter the next generation.

[0052] Perform a crossover operation on the selected parents to generate new candidate solutions.

[0053] Randomly mutate the weights w i and the dependence degrees D(X i ,Y) of some individuals, introducing randomness to avoid local optimization.

[0054] Continuously iterate the crossover and mutation until the fitness function converges.

[0055] The weights w i ' and the dependence degrees D(Xi,Y) optimized by the genetic algorithm are used in the final energy consumption prediction model, ensuring optimal energy scheduling and energy consumption prediction.

[0056] Taking into account the correlation, the dependence degree, and the weight adjustment optimized by the genetic algorithm, the final energy consumption prediction is represented as:

[0057]

[0058] wherein w i ' represents the weight optimized by the genetic algorithm, r dynamic,i (t) represents the dynamic correlation coefficient, and E i (t) is the energy consumption data of each system.

[0059] As a preferred scheme of the low-carbon park comprehensive energy data grabbing method, the method comprises the following steps: according to the correlation and dependency of the energy system and the overall energy consumption, a dynamic energy scheduling strategy is formulated, and systems with high correlation and large dependency are preferentially scheduled to reduce the use of inefficient energy.

[0060] In the peak load period, efficient and stable systems are scheduled, and in the valley load period, renewable energy such as photovoltaic systems is used to supplement energy supply, thereby optimizing the overall scheduling of the energy system.

[0061] Through long-term dependency analysis, inefficient or unstable systems are identified, and maintenance and upgrade suggestions are proposed. Regular maintenance is performed on inefficient systems to ensure their normal operation. For systems that have been inefficient for a long time, hardware upgrade suggestions are proposed.

[0062] 8 A low-carbon park comprehensive energy data grabbing system, characterized in that it comprises,

[0063] A preprocessing module acquires low-carbon park data and performs preprocessing.

[0064] An evaluation module comprehensively evaluates the correlation and dependency of the preprocessed data and the overall energy consumption.

[0065] An analysis module establishes an energy data model based on the correlation and dependency of the data and the overall energy consumption, and analyzes the dynamic change relationship between different energy types and the overall energy consumption of the park.

[0066] A suggestion module generates energy use optimization suggestions based on the dynamic change relationship.

[0067] A computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method as described above when executing the computer program.

[0068] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the method as described above.

[0069] The beneficial effects of the present application: the present application realizes the optimal management of low-carbon park comprehensive energy through four steps. By comprehensively evaluating the correlation and dependence of each energy system, the dynamic correlation coefficient based on time decay and the nonlinear dependence index are proposed, which effectively captures the timing changes and nonlinear dependence of different energy systems and the overall energy consumption, and realizes more accurate mutual influence evaluation between systems. By establishing an energy data model, the correlation and dependence are introduced into energy consumption prediction and scheduling optimization, and the genetic algorithm is used to dynamically adjust the system weight, providing an intelligent energy scheduling scheme, so that the load response of the energy system is more flexible and accurate. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0071] Figure 1 The overall flowchart of a low-carbon park comprehensive energy data grabbing method provided for the first embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0073] Embodiment 1, refer to Figure 1 For an embodiment of the present application, a low-carbon park comprehensive energy data grabbing method is provided, which comprises:

[0074] S1: Collect low-carbon park data and perform pretreatment.

[0075] It should be noted that in the low-carbon park, in order to reduce carbon emissions, clean energy such as solar energy, wind energy and geothermal energy should be actively used first. These renewable energy sources do not produce carbon dioxide emissions themselves and can gradually replace traditional fossil energy power supply and heating, thereby providing clean electricity and heat energy for daily operation of the building. On this basis, further data from various renewable energy systems (such as photovoltaic power generation, battery storage, ground source heat pump, water cold and heat storage systems) are collected and analyzed. The data are shown in Table 1.

[0076] Table 1 Data and collection method

[0077]

[0078]

[0079] Collecting basic data type system, photovoltaic system, battery system, heat pump system, water storage system, wind disk and lighting data in the park.

[0080] The basic data type system includes temperature.

[0081] The photovoltaic system includes current and temperature.

[0082] The battery system includes current, voltage, temperature, and remaining power.

[0083] The heat pump system includes water temperature and flow rate of primary side and secondary side.

[0084] The water storage system includes inlet and outlet water temperature and stratified water temperature.

[0085] The wind disk includes temperature and wind volume.

[0086] The lighting data includes illumination, brightness, and power.

[0087] The preprocessing includes data cleaning and data normalization.

[0088] The preprocessing includes filling missing values and data standardization.

[0089] The missing value filling method is mean filling, and the data standardization is represented as:

[0090]

[0091] where x i ′ represents the standardized data, x i represents the original data, x min represents the minimum value in the data, and x max represents the maximum value in the data.

[0092] S2: Comprehensive evaluation of the correlation and dependence of the preprocessed data and the overall energy consumption.

[0093] The correlation is used to measure the linear relationship between each energy system and the overall energy consumption, calculated by Pearson correlation coefficient, represented as:

[0094]

[0095] where x i represents the energy data of the i-th system, y i represents the overall energy consumption data at time i, represents the mean value of the system energy data, The overall energy consumption data mean value is represented.

[0096] The time-weighted dynamic correlation coefficient is introduced, and a decreasing exponential decay function is applied in the time dimension. The weight term is represented as:

[0097]

[0098] wherein a represents the time decay coefficient, t n represents the current time, t i represents the i-th time.

[0099] It should be noted that the time-weighted dynamic correlation coefficient is a method optimized on the basis of the Pearson correlation coefficient. The traditional Pearson correlation coefficient only considers the static linear relationship between variables, and cannot reflect the changes in the time dimension. In the energy system of the low-carbon park, the relationship between different energy systems and the overall energy consumption is constantly changing over time, so the time-weighted dynamic correlation coefficient is introduced, and the historical data is weighted using an exponential decay function. Through the decreasing exponential decay function, the data closer to the current time is given a higher weight, and the earlier data is given a lower weight, so as to more accurately reflect the real-time dynamic changes of the system.

[0100] Further, the exponential decay function enables the model to prioritize the data of the most recent time, effectively capturing the dynamic correlation of the energy system over time, and adapting to the rapidly changing load demand of the park. By attenuating the earlier data, the interference of historical data on the analysis of the present time can be reduced, and the real-time performance and accuracy of the system can be improved. The optimized dynamic correlation coefficient can better reflect the energy consumption contribution of the current energy systems, and help the dispatching system to preferentially select the energy systems with strong correlation, thereby improving the overall energy efficiency.

[0101] The Pearson correlation coefficient after introducing time decay is represented as:

[0102]

[0103] The Pearson correlation coefficient after introducing time decay has a value range of -1-1, close to 1 indicating strong positive correlation between the energy system and the overall energy consumption, close to -1 indicating strong negative correlation, and close to 0 indicating no correlation.

[0104] The dependency degree reflects the influence of a certain system on the overall energy consumption, and whether the system can determine the change of the overall energy consumption. The conditional entropy and mutual information based on information theory are used for calculation. The original conditional entropy formula is the uncertainty of system X under the premise of knowing system Y, represented as:

[0105] H(X|Y(=H(X|Y)-H(Y)

[0106] Where H(X|Y) represents conditional entropy, indicating the uncertainty of X given Y, H(X,Y) represents joint entropy, indicating the uncertainty of X and Y together, and H(Y) represents the entropy of Y, indicating the uncertainty of Y.

[0107] A nonlinear dependence index is introduced to enhance the capture of nonlinear dependence relationships by adding a mutual information filter function, denoted as:

[0108]

[0109] Where D(X,Y) represents the dependence of system X on the overall energy consumption Y, I(X;Y) represents the mutual information between X and Y, capturing the information dependence relationship between the two. H(X|Y i-1 ) represents the conditional entropy at the previous time, which is used to construct the time series dependence relationship of the system. β represents the contribution of mutual information to the dependence, ensuring that the dependence is higher when the mutual information is larger. N represents the total number of sampling points.

[0110] The optimized dependence value ranges from 0 to 1, where 0 represents no dependence relationship and 1 represents complete dependence relationship. The closer the value is to 1, the greater the impact of the system on the overall energy consumption.

[0111] It should be noted that the core of the optimized dependence is to construct it based on the conditional entropy and mutual information of information theory, combined with the time dimension and nonlinear dependence relationship between systems. On this basis, a mutual information filter function is introduced to strengthen the nonlinear relationship capture ability between the system and the overall energy consumption. The optimized dependence can reflect the contribution of a certain system to the overall energy consumption at the current time period by adjusting the weight and time sequence relationship, rather than just a simple linear dependence relationship.

[0112] Further, the optimized dependence model can handle complex interaction relationships between multiple systems, especially nonlinear dependence relationships, which helps to more accurately assess the impact of systems on the overall energy consumption. The combination of conditional entropy and mutual information with time dependence can reflect the change in dependence of the system at different times, enabling the system to more accurately schedule energy. The optimized dependence can dynamically evaluate the contribution of the system, so as to adjust energy use according to real-time demand and avoid excessive dependence on a system causing energy waste.

[0113] S3: Based on the correlation and dependence of data and overall energy consumption, an energy data model is established to analyze the dynamic change relationship between different energy types and the overall energy consumption of the park.

[0114] Based on the correlation and dependence, a comprehensive energy consumption model is constructed to analyze the dynamic change relationship between different energy types and the overall energy consumption of the park.

[0115] The overall energy consumption is calculated based on the correlation coefficient and the data of each energy system, represented as:

[0116]

[0117] wherein, E(t) represents the predicted overall energy consumption at time t, m represents the number of energy systems, w i i represents the weight of the i-th system, reflecting its influence on the overall energy consumption. dynamic,i r(t) represents the dynamic correlation coefficient of the i-th system at time t, E i (t) represents the energy consumption data of the i-th system at time t.

[0118] In dependency analysis, the energy consumption contribution in the model is adjusted according to the dependency of each system on the overall energy consumption. Dependency is used to adjust the influence degree of each system, ultimately affecting its weight. We adjust the weight in energy consumption prediction, represented as:

[0119]

[0120] wherein, w i ′ represents the weight of the i-th system after dependency adjustment, D(X i ,Y) represents the dependency of the i-th system on the overall energy consumption, w i represents the initial weight, the influence proportion of each system on the overall energy consumption without adjustment.

[0121] It should be noted that the comprehensive energy consumption model dynamically predicts the overall energy consumption of the park by integrating the correlation and dependency indicators. This model not only considers the historical data of each system, but also adjusts the weight of the system in real time through dynamic correlation coefficient and dependency. The adjustment of the weight is based on the real-time calculation of the dependency and correlation, ensuring that the contribution of each energy system is consistent with its actual influence on the overall energy consumption, thereby optimizing energy consumption prediction and energy scheduling strategy.

[0122] Genetic algorithm is introduced to optimize the weight w and dependency parameter in the model, making the overall energy consumption prediction more accurate and providing the optimal energy scheduling scheme.

[0123] A set of candidate solutions for weights {w i} and dependencies {D(X i ,Y)} are randomly generated, each representing a different energy scheduling scheme.

[0124] The fitness function f(w1,w2,…,w m ,D1,D2,…,D m ) is defined as the inverse of the energy consumption prediction error, aiming to minimize the prediction error, represented as:

[0125]

[0126] where E true (t) represents the actual energy consumption value, represents the predicted energy consumption value.

[0127] Select individuals with high fitness as parents to enter the next generation.

[0128] Perform crossover operation on the selected parents to generate new candidate solutions.

[0129] Randomly mutate the weights w i and dependency D(X i , Y) of part of the individuals to introduce randomness to avoid local optimum.

[0130] Continuously iterate crossover and mutation until the fitness function converges.

[0131] The weights w i and dependency D(Xi, Y) optimized by genetic algorithm are used in the final energy consumption prediction model to ensure optimal energy scheduling and energy consumption prediction.

[0132] It should be noted that the introduction of heuristic algorithm is to solve the optimization problem in the complex scheduling of multi-energy system. The traditional energy scheduling model is based on fixed weights and linear optimization, which is difficult to cope with the complex interaction and dynamic changes between multiple systems. Genetic algorithm generates multiple candidate solutions randomly and iteratively optimizes them through selection, crossover and mutation operations, gradually finding the energy scheduling scheme with the lowest energy consumption and the best performance. In genetic algorithm, the fitness function is used to measure the energy consumption performance of each candidate solution, guiding the algorithm to converge to the optimal solution.

[0133] Further, heuristic algorithm can handle complex nonlinear and multivariate optimization problems, breaking through the limitations of traditional linear models and being particularly suitable for complex energy scheduling problems between multiple systems. Through multiple iterations of optimization, heuristic algorithm can effectively avoid local optimum problem and find the globally optimal scheduling scheme, thereby minimizing the overall energy consumption of the park. Heuristic algorithm can flexibly adjust the energy scheduling strategy according to different time periods and load demands, improving the overall energy management efficiency and flexibility of the park.

[0134] Considering the correlation, dependency and weight adjustment optimized by genetic algorithm, the final energy consumption prediction is represented as:

[0135]

[0136] where w i ' represents the weight optimized by genetic algorithm, r dynamic,i (t) represents the dynamic correlation coefficient, Ei (t) is the energy consumption data of each system.

[0137] S4: Generate energy use optimization suggestions according to dynamic change relationship.

[0138] According to the correlation and dependence of the energy system and the overall energy consumption, a dynamic energy scheduling strategy is developed, which prioritizes systems with high correlation and dependence on overall energy consumption and reduces the use of inefficient energy.

[0139] During peak load periods, schedule efficient and stable systems, and during off-peak load periods, use renewable energy such as photovoltaic systems to supplement energy supply, optimizing the overall scheduling of energy systems.

[0140] Through long-term dependence analysis, identify inefficient or unstable systems and propose maintenance and upgrade recommendations. Regularly maintain inefficient systems to ensure their normal operation. For systems that have been inefficient for a long time, propose hardware upgrade recommendations.

[0141] The computer device can be a server. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data cluster data of the power monitoring system. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a low-carbon park comprehensive energy data grabbing method.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, databases or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0143] In one embodiment of the present application, a low-carbon park comprehensive energy data crawling method and system are provided. To verify the beneficial effects of the present application, a simulation experiment is conducted for scientific demonstration.

[0144] In this experiment, a comparative study of the low-carbon park comprehensive energy data crawling method is conducted to verify the innovation and advantages of the present application compared with the prior art. The experiment is divided into two parts, one part uses the existing energy management system, and the other part uses the low-carbon park comprehensive energy data crawling method of the present application. The test objects include photovoltaic systems, battery systems, heat pump systems, water storage systems and other energy systems in the park, and the parameters cover temperature, current, voltage, energy consumption, etc.

[0145] The prior art mainly relies on independent energy data monitoring solutions for each system, collecting data including current and temperature of photovoltaic power generation, voltage and remaining power of battery system, water temperature of heat pump system, etc. After the data collection is completed, the historical data is calculated for energy consumption by static analysis software.

[0146] Based on the low-carbon park comprehensive energy data grabbing method of the present application, the experiment first acquires the data of photovoltaic, battery, heat pump and other systems through a multi-dimensional data acquisition module, and performs data preprocessing including data cleaning, normalization and missing value filling. Subsequently, based on the Pearson correlation coefficient and the time-weighted dynamic correlation coefficient, the dynamic changes of each energy system and the overall energy consumption are evaluated. In terms of dependence calculation, the conditional entropy is used in the experiment in combination with the mutual information filter function to capture the nonlinear dependence relationship between systems. In the final scheduling stage, the genetic algorithm is introduced to realize the priority scheduling of efficient energy by dynamically adjusting the weight of each system, thereby minimizing the overall energy consumption and optimizing energy use. The experimental results are shown in Table 1.

[0147] Table 1 Experimental results

[0148]

[0149] Through the comparison of table data, it can be clearly seen that the present application has significant advantages. First, in terms of total energy consumption, the low-carbon park comprehensive energy data grabbing method of the present application can effectively reduce energy consumption, with the energy consumption of the photovoltaic system and the battery system reduced by 12.5% and 8.2% respectively, due to the optimization of the time-weighted dynamic correlation coefficient and the genetic algorithm of the present application, making the energy scheduling more accurate. Second, in the comparison of system correlation and dependence, the prior art is only based on static analysis of historical data, with the correlation coefficients of the photovoltaic and battery systems being 0.65 and 0.55 respectively, while the present application uses the optimization of time weighting and conditional entropy, with the correlation coefficients increased to 0.75 and 0.70, and the dependence also increased accordingly, reflecting the higher influence of the system on the overall energy consumption. This improvement makes the energy scheduling more dynamic, and can respond in real time to the changes in the load demand of the park.

[0150] The peak period energy consumption data also demonstrates the advantages of the present application. In the traditional system, the energy consumption during the peak period usually increases significantly, while the scheduling strategy of the present application preferentially uses efficient systems and reduces the participation of inefficient systems, resulting in a reduction of about 10% in peak period energy consumption. In addition, the improvement in scheduling flexibility score indicates that the present application significantly improves the real-time response capability of energy scheduling through the heuristic optimization of the genetic algorithm, with the scheduling response time shortened from 120 seconds to 80 seconds.

[0151] The fixed scheduling strategy in the prior art is difficult to cope with the fluctuation of the park load, resulting in a high system load fluctuation rate (20% for the battery system), while the present application greatly reduces the system load fluctuation rate to 8% through nonlinear dependence adjustment, ensuring the stability of energy supply. This stable load response greatly reduces equipment wear and tear and the dramatic fluctuation of energy consumption peaks and valleys, further improving the energy use efficiency and equipment life of the park.

[0152] In summary, the experimental data clearly show the innovation of the present application in energy management and the obvious advantages in practical application. Through the optimized data model and scheduling algorithm, the present application not only improves the synergy between systems, but also effectively reduces the overall energy consumption, with significant economic benefits and environmental value.

[0153] In Example 3, an embodiment of the present application provides a low-carbon park comprehensive energy data acquisition system, which includes a preprocessing module that collects low-carbon park data and performs preprocessing. An evaluation module that comprehensively evaluates the correlation and dependence of the preprocessed data and overall energy consumption. An analysis module that establishes an energy data model based on the correlation and dependence of the data and overall energy consumption, and analyzes the dynamic change relationship between different energy types and overall energy consumption of the park. A suggestion module that generates energy use optimization suggestions based on the dynamic change relationship.

[0154] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and they should be included in the scope of the claims of the present application.

Claims

1. A method for capturing comprehensive energy data of a low-carbon park, characterized in that: include: Collect low-carbon park data and perform pre-processing; Comprehensively evaluate the correlation and dependence between pre-processed data and overall energy consumption; Based on the correlation and dependence between data and overall energy consumption, an energy data model is established to analyze the dynamic relationship between different energy types and the overall energy consumption of the park; Generate energy usage optimization suggestions based on dynamic change relationships.

2. The method for capturing comprehensive energy data of a low-carbon park according to claim 1, characterized in that: The collection of low-carbon park data and pre-processing includes collecting data on basic data types systems, photovoltaic systems, battery systems, heat pump systems, water storage systems, wind turbines, and lighting within the park; The basic data type system includes temperature; Photovoltaic systems include current and temperature; The battery system includes current, voltage, temperature, and remaining capacity; The heat pump system includes water temperature and flow on the primary and secondary sides; The water storage system includes inlet and outlet water temperatures and stratified water temperatures; The fan disk includes temperature and air volume; Lighting data includes illuminance, brightness, and power; Preprocessing includes data cleaning and data normalization; Preprocessing includes imputing missing values ​​and data normalization; The mean imputation method is used to fill missing values, and the data standardization is expressed as: Among them, x i ′ represents the standardized data, x i Represents the original data, x min Represents the minimum value in the data, x max Indicates the maximum value in the data.

3. The method for capturing comprehensive energy data of a low-carbon park according to claim 2, characterized in that: The correlation and dependence between the pre-processed data and the overall energy consumption of the comprehensive evaluation include: the correlation is used to measure the linear relationship between each energy system and the overall energy consumption, which is calculated using the Pearson correlation coefficient and is expressed as: Among them, x i Represents the energy data of the i-th system, y i Represents the overall energy consumption data at time i, Indicates the mean value of system energy data, Indicates the average value of overall energy consumption data; The time-weighted dynamic correlation coefficient is introduced, and a decreasing exponential decay function is applied in the time dimension. The weighted term is expressed as: Where α represents the time attenuation coefficient, t n represents the current time, t i represents the i-th moment; The Pearson correlation coefficient after introducing time decay is expressed as: The Pearson correlation coefficient after introducing time decay ranges from -1 to 1. A value close to 1 indicates a strong positive correlation between the energy system and overall energy consumption, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates no correlation.

4. The method for capturing comprehensive energy data of a low-carbon park according to claim 3, characterized in that: The correlation and dependence between the pre-processed data and the overall energy consumption are also included in the comprehensive evaluation. The dependence reflects the degree of influence of a certain system on the overall energy consumption and whether the system can determine the change in overall energy consumption. The calculation is performed using conditional entropy and mutual information based on information theory. The original conditional entropy formula is the uncertainty of system X under the premise of known system Y, which is expressed as: H(X|Y)=H(X|Y)-H(Y) Among them, H(X|Y) represents the conditional entropy, which represents the uncertainty of X when Y is known; H(X|Y) represents the joint entropy, which represents the common uncertainty of X and Y; H(Y) represents the entropy of Y, which represents the uncertainty of Y; A nonlinear dependency index is introduced to enhance the capture of nonlinear dependencies by adding a mutual information filter function, which is expressed as: Among them, D(X,Y) represents the dependence of system X on the overall energy consumption Y, I(X;Y) represents the mutual information between X and Y, capturing the information dependency between the two; H(X|Y i-1 ) represents the conditional entropy of the previous moment, which is used to construct the temporal dependency of the system; β represents the contribution of the control mutual information to the dependency, ensuring that the dependency is higher when the mutual information is larger; N represents the total number of sampling points; The optimized dependency value range is 0 to 1, where 0 indicates no dependency and 1 indicates complete dependency; the closer the value is to 1, the greater the impact of the system on the overall energy consumption.

5. The method for capturing comprehensive energy data of a low-carbon park according to claim 4, characterized in that: The energy data model is established by constructing a comprehensive energy consumption model based on correlation and dependency, and analyzing the dynamic relationship between different energy types and the overall energy consumption of the park; The overall energy consumption is calculated based on the correlation coefficient and the data of each energy system, which is expressed as: in, represents the predicted overall energy consumption at time t, m represents the number of energy systems, and w i represents the weight of the i-th system, reflecting its impact on the overall energy consumption; r dynamic,i (t) represents the dynamic correlation coefficient of the i-th system at the t-th moment, E i (t) represents the energy consumption data of the i-th system at the t-th moment; In dependency analysis, the energy consumption contribution in the model is adjusted based on the dependency of each system on the overall energy consumption. Dependency is used to adjust the influence of each system, which ultimately affects its weight. We perform nonlinear adjustment on the weight in energy consumption prediction, which can be expressed as: Among them, w i ′ represents the weight of the ith system after dependency adjustment, D(X i ,Y) represents the dependence of the i-th system on the overall energy consumption, w i Represents the initial weight, which is the proportion of each system's impact on the overall energy consumption when not adjusted.

6. The method for capturing comprehensive energy data of a low-carbon park according to claim 5, characterized in that: The energy data model establishment also includes introducing a genetic algorithm to optimize the weight w and dependency parameters in the model to make the overall energy consumption forecast more accurate and provide an optimal energy scheduling solution; Randomly generate a set of weights {w i } and the dependency {D(X i ,Y)} candidate solutions, each solution represents a different energy scheduling scheme; Define the fitness function f(w1,w2,…,w m ,D1,D2,…,D m ) is the inverse of the energy consumption prediction error. The goal is to minimize the prediction error, which can be expressed as: Among them, E true (t) represents the actual energy consumption value, Indicates the predicted energy consumption value; Select individuals with high fitness as parents and enter the next generation; Perform crossover operations on the selected parent generations to generate new candidate solutions; The weight w of some individuals of random mutation i and the dependency D(X i ,Y), introduce randomness to avoid local optimality; Continuously iterate crossover and mutation until the fitness function converges; The weight w after optimization by genetic algorithm i ' and the dependency D(Xi,Y) are used in the final energy consumption prediction model to ensure optimal energy scheduling and energy consumption prediction; Taking into account the correlation, dependency and weight adjustment after genetic algorithm optimization, the final energy consumption prediction is expressed as: Among them, w i ' represents the weight after genetic algorithm optimization, r dynamic,i (t) represents the dynamic correlation coefficient, E i (t) is the energy consumption data of each system.

7. The method for capturing comprehensive energy data of a low-carbon park according to claim 6, characterized in that: The generating of energy usage optimization suggestions based on the dynamic change relationship includes: Based on the correlation and dependence of energy systems with overall energy consumption, a dynamic energy scheduling strategy is formulated to prioritize systems that are highly correlated with and dependent on overall energy consumption, thereby reducing the use of inefficient energy. During peak load periods, an efficient and stable system is dispatched, and during off-load periods, renewable energy such as photovoltaic systems are used to supplement energy supply, optimizing the overall dispatch of the energy system. Through long-term dependency analysis, we identify inefficient or unstable systems and make maintenance and upgrade recommendations. We regularly maintain inefficient systems to ensure their normal operation. For systems that have been performing inefficiently for a long time, we make hardware upgrade recommendations.

8. A low-carbon park integrated energy data capture system using the method according to any one of claims 1 to 7, characterized in that: Preprocessing module, collects low-carbon park data and performs preprocessing; Evaluation module, which comprehensively evaluates the correlation and dependence between pre-processed data and overall energy consumption; The analysis module establishes an energy data model based on the correlation and dependence between data and overall energy consumption, and analyzes the dynamic relationship between different energy types and the overall energy consumption of the park; The suggestion module generates energy usage optimization suggestions based on dynamic change relationships.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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